Data Scientist

National-Institutes-of-Health; Maryland; On-site; USD 78,000 - 105,000; Full time

Job summary

The National Institutes of Health seeks motivated Data Scientists to support the NIH mission across multiple locations.

This GS-1560 Data Scientist opportunity requires a bachelor's degree (or higher) in mathematics, statistics, computer science, data science, or related field, or an equivalent combination of education and experience. Applicants should be proficient with R, Python, and data visualization tools to extract insights and present findings to internal stakeholders.

Qualifications

• Bachelor's or graduate degree in mathematics, statistics, computer science, data science, or related field; or education plus related experience (30 semester hours)
• Education must be from an accrediting body recognized by US ED unless completed outside the US
• GS-11 level requires either 1 year of specialized experience at GS-09 level or 3 years of grad education toward PhD, or a combination meeting requirements

Skills

Data wrangling

Statistical analysis

Data visualization

Logistic regression

ANOVA

Education

Bachelor's degree in mathematics, statistics, computer science, data science or related field

Graduate degree in related field (Master/PhD)

Tools

R

Python

MATLAB

Tableau

Job description

The NIH's mission is to seek fundamental knowledge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce illness and disability. We are looking for motivated Data Scientists in multiple locations to help drive the NIH mission.

The following Institutes/Centers are participating in this recruitment:

NIBIB

NEI

DTR

Additional institutes/centers may also participate in this announcement.

Basic Requirements

To qualify for a Data Scientist, GS-1560 position, you must have a bachelor's degree, or graduate/higher level degree with a major study in mathematics, statistics, computer science, data science, or a field that is directly related to the position; OR, a combination of education (at least 30 semester hours) and additional appropriate/related experience that is the equivalent to a major in mathematics, statistics, computer science, data science or a field directly related to the position.

All education, unless completed outside of the U.S., must be from an educational program from an accrediting body recognized by the U.S. Department of Education at the time the education was completed.

Additional Requirements (GS-11)

In addition, in order to qualify for this position at the GS-11 level, you must have:
At least one year of specialized experience at or equivalent to the GS-09 level in the Federal service, obtained in either the private or public sector performing the following types of tasks: developing recommendations for the most efficient ways to collect quantitative information; creating data cleaning/wrangling scripts to transform data into a suitable format for analysis or storage; analyzing data with programming languages (e.g., R, Python) using statistical methods/techniques (e.g., Logistic Regression, ANOVA) to extract insights/findings; and summarizing data analysis results using data visualization tools to prepare presentations for internal stakeholders or team members; or,

3 years of progressively higher-level graduate education leading to a Ph.D. degree or Ph.D. or equivalent doctoral degree in a related field; or,

A combination of post baccalaureate education above the master's level in a related field, and experience that meets 100% of the qualification requirements for this position.

Additional Requirements (GS-12)

• Performing data collection, preprocessing, and analysis by applying programming languages (e.g., R, Python), feature engineering, advanced statistical methods/techniques (e.g., MANOVA, Bayesian Analysis), and machine learning/AI tools;
• Implementing machine learning/AI tools (e.g., NLP, DP, RL) to extract insights from data; and,
• Summarizing data analysis results using data visualization tools, programming languages, and software applications (e.g., MATLAB, Tableau) to prepare presentations for external stakeholders.

Additional Requirements (GS-13)

• Enhancing data collection, preprocessing, and analysis activities by implementing/adapting programming language codes (e.g., R, Python), feature engineering, advanced statistical methods/techniques (e.g., MANOVA, Bayesian Analysis), and machine learning/AI tools;
• Adapting and implementing machine learning and Artificial Intelligence techniques (e.g., NLP, Deep Learning, Reinforcement Learning) to extract insights from unstructured data;
• Explaining findings using data visualization tools, programming languages, and software applications (e.g., MATLAB, Tableau); and,
• Explaining data science concepts to stakeholders (e.g., researchers, leadership) to build consensus in planning data initiatives (e.g., data collection, preprocessing, analysis, reporting).

You will receive credit for all experience material to the position, including experience gained in religious, civic, welfare, service, and organizational activities, regardless of whether you received pay.

Do not copy and paste the duties, specialized experience, or occupational assessment questionnaire from this announcement into your resume as that will not be considered a demonstration of your qualifications for this position.

To determine your qualifications and referral status, we may review your resume and supporting documentation and compare it against your responses to the vacancy questionnaire. Ensure you support your self-ratings with the information you provide in your application. We may verify or assess your qualifications at any time. Inflated or unsupported qualifications may affect your rating. Any misrepresentation or material omission of facts may be sufficient cause to end further consideration of your candidacy. Persons listed as knowing your past accomplishments or experience in your application may be contacted for verification purposes at any time. Verification may, but need not, begin before receiving an offer.